In digital imaging and computer vision, feature detection is a fundamental operation that is typically a preliminary step to feature-based algorithms such as motion estimation, stabilization, image registration, object tracking, and depth estimation. The performance of these algorithms depends sensitively on the quality of the feature point estimates.
Various types of image features include edges, corners or interest points, and blobs or regions of interest. Edges are points where there is a boundary between two image regions, and are usually defined as sets of points in the image which have a strong gradient magnitude. Corners or interest points can refer to point-like features in an image that have a local two dimensional structure. A corner can be the intersection of two edges, or a point for which there are two dominant and different edge directions in a local neighborhood of the point. An interest point can be a point which has a well-defined position and can be robustly detected, such as a corner or an isolated point of local maximum or minimum intensity. Blobs or regions of interest can describe a type of image structure in terms of regions, which often contain a preferred point. In that sense, many blob detectors may also be regarded as interest point operators.
A simple but computationally intensive approach to corner detection is using correlation. Other methods include the Harris & Stephens corner detection algorithm that considers the differential of the corner score with respect to direction using the sum of squared differences.
Achieving effective feature detection depends in part on providing high quality data, i.e., high resolution image(s), to the feature detector.